皇冠梨糖度可見/近紅外光譜在線檢測模型傳遞研究

徐惠榮 李青青

(1.浙江大學(xué)生物系統(tǒng)工程與食品科學(xué)學(xué)院， 杭州 310058； 2.農(nóng)業(yè)部農(nóng)產(chǎn)品產(chǎn)地處理裝備重點(diǎn)實(shí)驗(yàn)室， 杭州 310058)

皇冠梨糖度可見/近紅外光譜在線檢測模型傳遞研究

徐惠榮1,2李青青1

(1.浙江大學(xué)生物系統(tǒng)工程與食品科學(xué)學(xué)院， 杭州 310058； 2.農(nóng)業(yè)部農(nóng)產(chǎn)品產(chǎn)地處理裝備重點(diǎn)實(shí)驗(yàn)室， 杭州 310058)

在水果內(nèi)部品質(zhì)檢測分級實(shí)際生產(chǎn)中往往存在多通道測量，由于儀器不同或加工精度不同而存在多通道間檢測模型不具通用性問題，應(yīng)用多種模型傳遞方法研究了在線檢測條件下兩個(gè)不同可見/近紅外光譜儀間的皇冠梨糖度預(yù)測模型傳遞及預(yù)測比較分析。結(jié)果表明：從儀器的光譜數(shù)據(jù)經(jīng)直接校正算法(DS)和基于平均光譜差值校正的DS算法(MSSC-DS)轉(zhuǎn)換后用于主儀器所建模型的預(yù)測結(jié)果相對較好，預(yù)測均方根誤差小于0.5°Brix，可以滿足實(shí)際生產(chǎn)。但通過模型轉(zhuǎn)換后的預(yù)測結(jié)果均比利用從儀器數(shù)據(jù)直接建模的預(yù)測結(jié)果要差(預(yù)測均方根誤差為0.381°Brix)，因而在實(shí)際生產(chǎn)中，需要從成本和分級精度的要求來考慮選擇建模的方式。

皇冠梨； 糖度； 在線檢測； 可見/近紅外光譜； 模型傳遞

引言

可見/近紅外光譜分析技術(shù)已被廣泛用于鮮果內(nèi)部品質(zhì)的無損檢測[1-16]，但在水果內(nèi)部品質(zhì)檢測分級生產(chǎn)線實(shí)際應(yīng)用中往往存在多通道測量，面臨由于儀器不同或加工精度不同而存在多通道間檢測模型不具通用性問題[17]。模型傳遞也稱為儀器標(biāo)準(zhǔn)化，是指通過化學(xué)計(jì)量學(xué)方法，建立主、從儀器之間的數(shù)學(xué)關(guān)系，使主儀器上建立的校正模型，能夠在從儀器上有效地預(yù)測新樣品，從而減少重新建模所帶來的工作量。模型傳遞最早由OSBORNE等[18]提出，并建立了斜率/偏差算法(Slope/bias，S/B)，并由SHENK[19]、WANG等[20]相繼提出了新的模型傳遞算法，即Shenk’s算法和分段直接校正算法(Piecewise direct standardization，PDS)，之后，國內(nèi)外學(xué)者在模型傳遞算法上進(jìn)行了大量的研究[21-33]。近年來，已有少量文獻(xiàn)報(bào)道用于水果糖度預(yù)測模型的傳遞研究。胡潤文等[31]通過S/B算法和直接校正(Direct standardization，DS)算法實(shí)現(xiàn)了臍橙總糖模型在相同型號儀器間的傳遞。SALGUERO-CHAPARRO等[32]采用S/B算法、PDS算法和正交投影轉(zhuǎn)換法將橄欖脂肪、游離酸含量以及含水率檢測模型從靜態(tài)儀器傳遞到了便攜式儀器。吉納玉等[33]采用DS算法實(shí)現(xiàn)了蘋果糖度模型在相同型號便捷式近紅外儀器之間的傳遞。

在水果品質(zhì)在線實(shí)時(shí)檢測分級中，對水果糖度無損檢測模型穩(wěn)健性影響的因素還來自樣品相關(guān)因素和其他非樣品相關(guān)因素[34]，本文探討在利用可見/近紅外光譜進(jìn)行梨糖度在線實(shí)時(shí)檢測時(shí)不同小型光纖光譜儀之間模型傳遞的可行性。

1 材料與方法

1.1 材料

實(shí)驗(yàn)所用樣本為河北省滄州皇冠梨，從杭州水果批發(fā)市場購買。選擇大小與果形相近的皇冠梨，對其進(jìn)行表面清潔并標(biāo)號后，在實(shí)驗(yàn)室條件(溫度約23℃、相對濕度約70%)下放置1 d，使樣本內(nèi)外溫度一致，然后在線實(shí)時(shí)采集梨的可見/近紅外半透射光譜，并測量糖度。

分別采用美國海洋光學(xué)公司生產(chǎn)的QE65Pro型和QE65000型微型光纖光譜儀采集同一批水果光譜，兩款光譜儀都采用相同的濱松背照式CCD面陣探測器，可以合并同列垂直像素，大幅提高信噪比(>1 000)，QE65Pro型光譜儀配置HC-1型光柵和OFLV-QE-400型濾波器，波長范圍396.8～1 174.0 nm，共1 044像素，并安裝有100 μm狹縫(入口孔徑)。QE65000型光譜儀配置HC-1型光柵和OFLV-QE-250型濾波器，波長范圍247.9～1 040.7 nm，共1 044像素，未安裝狹縫(入口孔徑即為光纖芯徑1 000 μm)。

1.2 儀器與方法

圖1為自行設(shè)計(jì)的水果內(nèi)部品質(zhì)可見/近紅外光譜在線實(shí)時(shí)檢測系統(tǒng)，配置了基于C++語言自行開發(fā)的光譜數(shù)據(jù)采集記錄軟件。實(shí)驗(yàn)時(shí)，輸送帶速度為0.5 m/s。為了減少暗電流及光源隨時(shí)間變化的影響，在采集樣本光譜前，先采集暗場光譜(即在關(guān)閉光路的情況下采集暗電流值)以及參比光譜(參比采用直徑為75 mm的Teflon球)。水果光譜采集時(shí)，水果放置在自由輸送托盤上，由輸送帶傳輸至光照箱正中兩側(cè)光源(左、右兩側(cè)各裝有一只150 W鹵鎢燈)之間，當(dāng)托盤底座開口(直徑35 mm)與準(zhǔn)直鏡大端接口(直徑25 mm)剛接觸時(shí)，光電傳感器通過自行設(shè)計(jì)的控制電路觸發(fā)光譜儀進(jìn)行光譜采集，積分時(shí)間為100 ms。準(zhǔn)直鏡經(jīng)光纖與光譜儀相連，光譜儀將得到的攜帶有水果信息的光譜信號通過USB數(shù)據(jù)線發(fā)送給計(jì)算機(jī)，進(jìn)行實(shí)時(shí)處理并記錄光譜數(shù)據(jù)。光譜記錄時(shí)通過軟件直接采用Boxcar平滑法(平滑點(diǎn)數(shù)為6)進(jìn)行光譜平滑預(yù)處理。

圖1 自由托盤輸送的水果內(nèi)部品質(zhì)在線實(shí)時(shí)檢測系統(tǒng)Fig.1 Free tray based on-line detection system for fruit internal quality1.計(jì)算機(jī) 2.光纖 3.光譜儀 4.控制電路 5.準(zhǔn)直鏡 6.位置傳感器 7.鹵鎢燈 8.輸送帶 9.輸送托盤 10.光照箱 11.皇冠梨

理化分析測定糖度時(shí)，參照NY/T 2637—2014，將水果去核切成小塊，放入榨汁機(jī)中榨取果汁并進(jìn)行過濾，用手持式糖量計(jì)(PR-101型數(shù)字式折射儀，日本ATAGO公司)進(jìn)行測量，將2次測量平均值作為其糖度。

1.3 模型傳遞方法

1.3.1斜率/偏差算法

S/B算法是基于預(yù)測結(jié)果的校正，假設(shè)主儀器和從儀器上測得的預(yù)測值之間存在線性關(guān)系，其基本過程如下[18]：

(1)將主儀器上建立的校正模型T直接應(yīng)用于從儀器，選擇m個(gè)樣品，在主儀器和從儀器上分別測得其光譜矩陣Xms和Xss，根據(jù)校正模型計(jì)算得到成分預(yù)測矩陣Cmp和Csp，計(jì)算公式為

(1)

(2)假設(shè)成分預(yù)測矩陣Cmp和Csp之間存在線性關(guān)系，并通過最小二乘法計(jì)算得到截距wbias和斜率sslop，公式為

Cmp=wbias+sslopCsp

(2)

(3)對于從儀器上測得的未知成分含量的樣品光譜Xss,un，根據(jù)式(2)可直接預(yù)測成分含量Cpsp,un，即

Cpsp,un=wbias+sslop(Xss,unT)

(3)

1.3.2直接校正算法

DS算法是一種有標(biāo)傳遞算法，其基本流程如下[20]：在主儀器和從儀器上測得的某一樣品集光譜矩陣分別為Xms和Xss，其維數(shù)為m×p，p為波長點(diǎn)個(gè)數(shù)，可建立兩者轉(zhuǎn)換運(yùn)算公式

Xms=XssF+B′s

(4)

式中F——維數(shù)p×p的轉(zhuǎn)換矩陣B′s——維數(shù)p×1的背景校正矩陣的轉(zhuǎn)置

若未知成分含量的樣品在從儀器上測定的光譜為Xss,un，則根據(jù)式(4)可轉(zhuǎn)換得到適合于主儀器所建模型T的光譜數(shù)據(jù)

Xpss,un=Xss,unF+B′s

(5)

1.3.3分段直接校正算法

PDS算法與DS算法的原理基本相同，不同之處在于，在DS算法中，從儀器樣品光譜矩陣采用的是全波長數(shù)據(jù)Xss,all(下角標(biāo)all表示所有波長)來擬合主儀器樣品光譜矩陣Xms的每一個(gè)波長點(diǎn)數(shù)據(jù)Xms,i(下角標(biāo)i表示第i個(gè)波長)，而在PDS算法中，采用的是波長點(diǎn)附近一窗口大小為(j+k+1)的光譜段Xss,j+k+1來擬合Xms,i。

1.3.4平均光譜差值校正算法

平均光譜差值校正(MSSC)算法是一種運(yùn)算簡捷，且在實(shí)際應(yīng)用中易于實(shí)現(xiàn)的光譜校正方法，最早被用于消除在線多通道近紅外分析儀各通道間的光譜差異。操作過程如下[17]：

首先選取m個(gè)樣本，在各個(gè)儀器(通道)上采集光譜，組成校正光譜陣Xi(i=1，2，…，n，n為儀器數(shù)或者通道數(shù))，每臺(tái)儀器的平均光譜向量計(jì)算公式為

(6)

(7)

對第i臺(tái)儀器(或者通道)測量的光譜進(jìn)行修正的公式為

(8)

1.4 模型評價(jià)指標(biāo)

在近紅外光譜建模分析中，通常把樣本分成校正集和預(yù)測集兩部分，用校正相關(guān)系數(shù)rcal和校正均方根誤差來評價(jià)校正精度，用預(yù)測相關(guān)系數(shù)rpre和預(yù)測均方根誤差來評價(jià)預(yù)測精度，并用相對分析誤差來判斷模型的好壞，該指標(biāo)是用標(biāo)準(zhǔn)偏差除以預(yù)測均方根誤差得到的，MALLEY等[35]提出：高精度模型的相對分析誤差在4以上；成功模型的相對分析誤差在3～4范圍內(nèi)；比較成功模型的相對分析誤差在2.25～3范圍內(nèi)；比較有用模型的相對分析誤差在1.75～2.25范圍內(nèi)。

2 結(jié)果與討論

圖2為皇冠梨樣本通過QE65000型光譜儀和QE65Pro型光譜儀采集的550～920 nm波長范圍內(nèi)的原始光譜及平均光譜?？傮w上看，兩臺(tái)儀器采集的光譜相似，可看出微小的吸光度差異和波長漂移，QE65000型光譜儀采集的數(shù)據(jù)吸光度略大，且光譜數(shù)據(jù)噪聲小。

2.1 PLSR建模分析

先對兩個(gè)光譜儀各自采集的數(shù)據(jù)分別進(jìn)行直接建模分析，剔除異常樣本后，用于各自PLSR建模分析的樣本數(shù)分別為199和200個(gè)(由于異常樣本不同，兩光譜儀數(shù)據(jù)并非嚴(yán)格一一對應(yīng))，通過K-Stone算法將樣本集按2∶1的比例劃分為校正集和預(yù)測集，劃分后的校正集和預(yù)測集樣品外觀形態(tài)和糖度檢測結(jié)果如表1所示。表2為兩光譜儀皇冠梨糖度PLSR模型校正和預(yù)測分析結(jié)果，從表中可以看出兩光譜儀數(shù)據(jù)各自直接建模校正均方根誤差都小于0.3°Brix，預(yù)測均方根誤差都小于0.4°Brix，且相對分析誤差大于2.2，所建模型較成功。圖3為兩光譜儀校正集和預(yù)測集樣本真實(shí)值和預(yù)測值的對比分布圖，QE65000型光譜儀模型的預(yù)測能力優(yōu)于QE65Pro型。

2.2 模型傳遞分析

圖4為兩光譜儀采集的皇冠梨光譜數(shù)據(jù)校正集樣本的前3個(gè)主成分分布圖。從圖4中可以看出，兩組數(shù)據(jù)主成分空間分布有一定的偏差，但整體分布呈線性平移，可見兩光譜儀采集的光譜數(shù)據(jù)差異有一定的規(guī)律可循。

圖2 兩臺(tái)儀器采集的原始光譜及其平均光譜Fig.2 Raw and average spectra obtained by two spectrometers

光譜儀樣本集樣本數(shù)量參數(shù)數(shù)值最小值最大值平均值標(biāo)準(zhǔn)偏差質(zhì)量/g222.00420.00309.9844.63校正集132橫徑/mm74.8892.7482.154.14縱徑/mm65.3090.3377.735.17QE65000型糖度/°Brix9.5013.4011.240.81質(zhì)量/g230.00420.00310.2542.06預(yù)測集67橫徑/mm76.0491.6882.463.80縱徑/mm65.7489.0277.004.92糖度/°Brix9.8013.3011.320.84質(zhì)量/g225.00420.00308.0944.33校正集133橫徑/mm74.8892.7481.934.08縱徑/mm65.7390.3377.624.87QE65Pro型糖度/°Brix10.0014.0011.260.86質(zhì)量/g233.00404.00309.1941.76預(yù)測集67橫徑/mm75.6190.6782.443.78縱徑/mm65.3089.2376.745.41糖度/°Brix9.5013.4011.280.84

表2 兩光譜儀檢測皇冠梨糖度PLSR模型分析結(jié)果Tab.2 Calibration and prediction results of two spectrometers using PLSR model for sugar content of crown pear

圖3 不同光譜儀皇冠梨可溶性固形物PLSR模型的檢測結(jié)果Fig.3 Scatter plots of measured and predicted sugar contents obtained by PLSR model

圖4 兩光譜儀校正集樣本的前3個(gè)主成分分布圖Fig.4 Distribution diagram of the first three PCs of calibration set of two spectrometers

設(shè)QE65000型光譜儀為主儀器，QE65Pro型為從儀器，研究比較了DS、PDS、S/B、MSSC算法對兩光譜儀間皇冠梨糖度在線檢測模型的傳遞。模型傳遞前后的預(yù)測結(jié)果如表3所示，從表中可以看出，用主儀器所建的校正模型直接預(yù)測從儀器的預(yù)測集樣本，所得預(yù)測均方根誤差為8.482°Brix，而通過DS算法進(jìn)行模型傳遞后，預(yù)測均方根誤差下降到了0.473°Brix，經(jīng)MSSC光譜差異校正后再進(jìn)行DS傳遞，預(yù)測結(jié)果得到了進(jìn)一步改善，預(yù)測均方根誤差為0.453°Brix，已經(jīng)達(dá)到一般生產(chǎn)實(shí)際的要求(小于0.5°Brix)。相比之下，其他模型傳遞算法沒有得到明顯的改善?？赡茉蚴荄S算法采用了所有波長點(diǎn)的數(shù)據(jù)進(jìn)行轉(zhuǎn)換，提高了光譜曲線的擬合精度，而MSSC算法更進(jìn)一步減少了兩臺(tái)光譜儀數(shù)據(jù)間的光譜差異。

表3 模型傳遞前后預(yù)測結(jié)果Tab.3 Prediction results before and after calibration model transfer

3 結(jié)束語

應(yīng)用多種模型傳遞方法(DS、PDS、S/B、MSSC、MSSC-DS、MSSC-PDS、MSSC-S/B)研究了在線檢測條件下(速度0.5 m/s)兩個(gè)不同可見/近紅外光譜儀間皇冠梨糖度預(yù)測模型傳遞及預(yù)測比較分析，結(jié)果表明：從儀器的光譜數(shù)據(jù)經(jīng)DS和MSSC-DS轉(zhuǎn)換后用于主儀器所建模型的預(yù)測結(jié)果相對較好，可以滿足實(shí)際生產(chǎn)，且通過光譜校正預(yù)處理(MSSC)消除或降低兩光譜差異可以進(jìn)一步提高預(yù)測精度。通過模型轉(zhuǎn)換后的預(yù)測結(jié)果均比利用兩光譜儀數(shù)據(jù)各自直接建模的結(jié)果要差，因而在實(shí)際生產(chǎn)中，需要從成本和分級精度的要求上來考慮選擇建模的方式。

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CalibrationModelTransferbetweenVisible/NIRSpectrometersinSugarContentOn-lineDetectionofCrownPears

XU Huirong1,2LI Qingqing1

(1.CollegeofBiosystemsEngineeringandFoodScience,ZhejiangUniversity,Hangzhou310058,China2.KeyLaboratoryofOnSiteProcessingEquipmentforAgriculturalProducts,MinistryofAgriculture,Hangzhou310058,China)

With the development of social economy and growth of people’s living standand, the demond of fruit quality is ever increasing. Quality detection and grading of postharvest fruit is an integral part of commoditization processing, which is also an effective way to achieve high price with good quality. Visible/NIR spectroscopy with the advantages of rapid, nondestructive and being on-line analyzing, has been widely used in agriculture. In the actual application of visible/NIR spectroscopy for on-line detection of fruit internal quality, multi-channels measurement often exists, in which the prediction model is not universal among multi channels due to different spectrometers or their different manufacture precisions. Calibration model transfer is a key problem in visible/NIR spectral quantitative analysis. Comparative analysis of some calibration model transfer methods, such as direct standardization (DS), piecewise direct standardization (PDS), slope/bias (S/B) between two different visible/NIR spectrometers (master and slave spectrometers, model QE65000 and QE65Pro, Ocean Optics, Inc., USA) in the sugar content on-line detection of crown pears was carried out at conveyor speed of 0.5 m/s. The results showed that the prediction values by DS algorithm and DS algorithm based on the mean spectra subtraction correction (MSSC-DS) were relatively good with low root mean square error of prediction (RMSEP) of less than 0.5°Brix, which can satisfy the industry application. And pre-processing method of MSSC can improve the prediction accuracy of calibration model transfer by eliminating and mitigating the differences between the spectra acquired on master and slave spectrometers. However, the best prediction result on salve instrument after calibration model transfer (RMSEP was 0.453°Brix) was still inferior to that predicted by the model developed directly using slave data (RMSEP was 0.381°Brix). Thus, in the actual application, appropriate modeling selection should be considered from the cost and the accuracy of classification.

crown pears; sugar content; on-line detection; visible/NIR spectroscopy; calibration transfer

O657.33; S661.2

A

1000-1298(2017)09-0312-06

10.6041/j.issn.1000-1298.2017.09.039

2017-03-16

2017-07-02

國家自然科學(xué)基金面上項(xiàng)目(31571562)

徐惠榮(1973—)，男，教授，博士生導(dǎo)師，主要從事農(nóng)產(chǎn)品品質(zhì)無損檢測技術(shù)與裝備研究，E-mail： hrxu@zju.edu.cn